# Mediating Modes of Thought: LLM's for design scripting

arXiv: https://arxiv.org/abs/2411.14485 · HTML: https://mlsanity.com/paper/2411.14485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 6 of 6)

- [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023)
- [Chain of Thought Empowers Transformers to Solve Inherently Serial Problems](https://mlsanity.com/paper/2402.12875/) (2024)
- [Premise Order Matters in Reasoning with Large Language Models](https://mlsanity.com/paper/2402.08939/) (2024)
- [StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows](https://mlsanity.com/paper/2403.11322/) (2024)
- [How Can Large Language Models Help Humans in Design and Manufacturing?](https://mlsanity.com/paper/2307.14377/) (2023)
- [Neurosymbolic Models for Computer Graphics](https://mlsanity.com/paper/2304.10320/) (2023)

## Built on by (most cited first, 1 of 1)

- [MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture](https://mlsanity.com/paper/2511.11587/) (2025)

## Abstract (arXiv)

Architects adopt visual scripting and parametric design tools to explore more expansive design spaces (Coates, 2010), refine their thinking about the geometric logic of their design (Woodbury, 2010), and overcome conventional software limitations (Burry, 2011). Despite two decades of effort to make design scripting more accessible, a disconnect between a designer's free ways of thinking and the rigidity of algorithms remains (Burry, 2011). Recent developments in Large Language Models (LLMs) suggest this might soon change, as LLMs encode a general understanding of human context and exhibit the capacity to produce geometric logic. This project speculates that if LLMs can effectively mediate between user intent and algorithms, they become a powerful tool to make scripting in design more widespread and fun. We explore if such systems can interpret natural language prompts to assemble geometric operations relevant to computational design scripting. In the system, multiple layers of LLM agents are configured with specific context to infer the user intent and construct a sequential logic. Given a user's high-level text prompt, a geometric description is created, distilled into a sequence of logic operations, and mapped to software-specific commands. The completed script is constructed in the user's visual programming interface. The system succeeds in generating complete visual scripts up to a certain complexity but fails beyond this complexity threshold. It shows how LLMs can make design scripting much more aligned with human creativity and thought. Future research should explore conversational interactions, expand to multimodal inputs and outputs, and assess the performance of these tools.